Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation

TL;DR

This paper introduces a local action-guided diffusion model for text-to-motion generation, enabling more diverse, realistic, and controllable global motions by leveraging local actions as fine-grained control signals.

Contribution

It proposes a novel local action-guided diffusion framework that improves motion diversity and control in text-to-motion synthesis by integrating local actions and guiding weights during the diffusion process.

Findings

Effective on HumanML3D and KIT datasets

Enhances motion diversity and user control

Demonstrates seamless local-global motion integration

Abstract

Text-to-motion generation requires not only grounding local actions in language but also seamlessly blending these individual actions to synthesize diverse and realistic global motions. However, existing motion generation methods primarily focus on the direct synthesis of global motions while neglecting the importance of generating and controlling local actions. In this paper, we propose the local action-guided motion diffusion model, which facilitates global motion generation by utilizing local actions as fine-grained control signals. Specifically, we provide an automated method for reference local action sampling and leverage graph attention networks to assess the guiding weight of each local action in the overall motion synthesis. During the diffusion process for synthesizing global motion, we calculate the local-action gradient to provide conditional guidance. This local-to-global paradigm reduces the complexity associated with direct global motion generation and promotes motion diversity via sampling diverse actions as conditions. Extensive experiments on two human motion datasets, i.e., HumanML3D and KIT, demonstrate the effectiveness of our method. Furthermore, our method provides flexibility in seamlessly combining various local actions and continuous guiding weight adjustment, accommodating diverse user preferences, which may hold potential significance for the community. The project page is available at https://jpthu17.github.io/GuidedMotion-project/.